The present invention relates to the efficient ranking and selective retrieval of anomalous events (anomalies) determined in visual image data.
Determining and recognizing anomalous motion activities (anomalies) in visual image data is useful in determining occurrences or absences of certain activities or events. For example, image data of structures may be monitored for changes in expected or normal visual data patterns that are indicative of events and behaviors diverging from norms, such as immediate or potential failures of structural components or human movements or activities outside of compliance with usual safety or other activity processes and policies. If readily distinctive to human analysis, such anomalies may be identified by capturing and recording visual data through still image and video systems for subsequent or contemporaneous analysis. However, with large amounts of data, discerning anomalies of importance from other anomalies may be difficult, time consuming or inefficient, and even non-feasible. More particularly, it is not enough to merely recognize that an anomaly has occurred in the context of high frequencies or numbers anomaly occurrences, especially if some otherwise equivalent anomalies may have more importance than others.
Automated video systems and methods are known wherein computers or other programmable devices directly analyze video data and attempt to recognize anomaly objects, people, events or activities of concern, etc., through identifying anomalous motion patterns through computer vision applications. However, discernment of more significant anomalies from other anomalies or even from normal patterns, events, etc., by automated video surveillance systems and methods systems is often not reliable in realistic, real-world environments and applications due to a variety of factors. For example, visual image data may be difficult to analyze or vary over time due to clutter, poor or variable lighting and object resolutions, distracting competing visual information, etc. False alerts or missed event recognitions must also occur at an acceptable level.